基于高光谱多参数的冷鲜牛肉品质快速检测技术

doi:10.3964/j.issn.1000-0593(2021)08-2572-06

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Abstract In order to solve the problems of complicated operation and irreversible damage of traditional chilled beef quality detection technology, this paper proposed a method of chilled beef quality detection based on hyperspectral fusion and multi-parameter fusion. The Region of Interest (ROI) spectra of chilled beef were extracted, and the texture parameters of chilled beef were measured: hardness, elasticity, adhesion, adhesion, chewing degree and resilience. After the parameter precision comparison, the cohesiveness and resilience are selected as the modeling parameters. Kennard-stone and the SPXY algorithms were used to divide the original spectral data respectively, and the optimal sample division method was determined by the prediction effect of the model built after sample division. Finally, 35 training sets and 7 test sets were obtained by dividing the samples by the SPXY algorithm. Based on the sample division of the SPXY algorithm, preprocessing of hyperspectral data was conducted by using first derivative (D1st), multiple scattering correction (MSC), second derivative (D2st) and standard normal transformation (SNV), which effectively eliminated the noise in the spectrum and improved the signal-to-noise ratio. The continuous projection method (SPA) is used to extract the spectral characteristic wavelength, which effectively reduces the shortcoming of the large amount of noise information contained in the full-band modeling, ensures the accuracy of the model and improves the running speed of the model. Finally, the partial least square method (PLSR) and principal component regression method (PCR) were used to construct the quality prediction model of chilled beef. When the cohesion was taken as the parameter, the SNV-SPA-PLSR model had the best performance, and the predicted correlation coefficient was 0.879 8. The D2st-SPA-PLSR model has the highest accuracy when regression is taken as the parameter, and the predicted correlation coefficient is 0.880 6. The experimental results show that the chilled meat quality detection method based on hyperspectral fusion and multi-parameter fusion can realize the fast quality detection of chilled beef.

Key words：Hyperspectral; Beef quality detection; Texture parameters; Partial least square method

Received: 2020-07-26 Accepted: 2020-11-30

ZTFLH:

TS251.7

Corresponding Authors: LI Xin-xing E-mail: lxxcau@cau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)08-2572-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I08/2572

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